Page 30 - Statistics for Dummies

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Part I: Vital Statistics about Statistics
on job satisfaction of Americans; if you call people at home during the day
between 9 a.m. and 5 p.m., you miss out on everyone who works during the
day. Maybe day workers are more satisfied than night workers.
You have to watch for bias when collecting survey data. For instance: Some
surveys are too long — what if someone stops answering questions halfway
through? Or what if they give you misinformation and tell you they make
$100,000 a year instead of $45,000? What if they give you answers that aren’t
on your list of possible answers? A host of problems can occur when collect-
ing survey data, and you need to be able to pinpoint those problems.
Experiments are sometimes even more challenging when it comes to bias and
collecting data. Suppose you want to test blood pressure; what if the instru-
ment you’re using breaks during the experiment? What if someone quits the
experiment halfway through? What if something happens during the experi-
ment to distract the subjects or the researchers? Or they can’t find a vein when
they have to do a blood test exactly one hour after a dose of a drug is given?
These problems are just some examples of what can go wrong in data collection
for experiments, and you have to be ready to look for and find these problems.
After you go through Chapter 16 (on samples and surveys) and Chapter 17
(on experiments), you’ll be able to select samples and collect data in an unbi-
ased way, being sensitive to little things that can really influence the results.
And you’ll have the ability to evaluate the credibility of statistical results and
to be heard, because you’ll know what you’re talking about.
Creating Effective Summaries
After good data have been collected, the next step is to summarize them to
get a handle on the big picture. Statisticians describe data in two major ways:
with numbers (called descriptive statistics) and with pictures (that is, charts
and graphs).
Descriptive statistics
Descriptive statistics are numbers that describe a data set in terms of its impor-
tant features:
✓ If the data are categorical (where individuals are placed into groups,
such as gender or political affiliation), they are typically summarized
using the number of individuals in each group (called the frequency) or
the percentage of individuals in each group (called the relative frequency).

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